Description

Book Synopsis
In the age of data-driven problem-solving, applying sophisticated computational tools for explaining substantive phenomena is a valuable skill. Yet, application of methods assumes an understanding of the data, structure, and patterns that influence the broader research program. This Element offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered in this Element, in addition to R code and real data to facilitate interaction with the concepts. Upon setting the stage for clustering, the following algorithms are detailed: agglomerative hierarchical clustering, k-means clustering, Gaussian mixture models, and at a higher-level, fuzzy C-means clustering, DBSCAN, and partitioning around medoids (k-medoids) clustering.

Table of Contents
1. Introduction; 2. Setting the stage for clustering; 3. Agglomerative hierarchical clustering; 4. k-means clustering; 5. Gaussian mixture models; 6. Advanced methods; 7. Conclusion.

Unsupervised Machine Learning for Clustering in

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    A Paperback by Philip D. Waggoner

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      Book details

      Publisher Cambridge University Press
      Published 1/28/2021 12:00:00 AM
      ISBN-13 9781108793384
      978-1108793384
      ISBN-10 110879338X
      Also in:
      Computing Data capture and analysis Database design and theory Social research and statistics

      Description

      Book Synopsis
      In the age of data-driven problem-solving, applying sophisticated computational tools for explaining substantive phenomena is a valuable skill. Yet, application of methods assumes an understanding of the data, structure, and patterns that influence the broader research program. This Element offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered in this Element, in addition to R code and real data to facilitate interaction with the concepts. Upon setting the stage for clustering, the following algorithms are detailed: agglomerative hierarchical clustering, k-means clustering, Gaussian mixture models, and at a higher-level, fuzzy C-means clustering, DBSCAN, and partitioning around medoids (k-medoids) clustering.

      Table of Contents
      1. Introduction; 2. Setting the stage for clustering; 3. Agglomerative hierarchical clustering; 4. k-means clustering; 5. Gaussian mixture models; 6. Advanced methods; 7. Conclusion.

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